Abstract
Explanations—a form of post-hoc interpretability—play an instrumental role in making systems accessible as AI continues to proliferate complex and sensitive sociotechnical systems. In this paper, we introduce Human-centered Explainable AI (HCXAI) as an approach that puts the human at the center of technology design. It develops a holistic understanding of “who” the human is by considering the interplay of values, interpersonal dynamics, and the socially situated nature of AI systems. In particular, we advocate for a reflective sociotechnical approach. We illustrate HCXAI through a case study of an explanation system for non-technical end-users that shows how technical advancements and the understanding of human factors co-evolve. Building on the case study, we lay out open research questions pertaining to further refining our understanding of “who” the human is and extending beyond 1-to-1 human-computer interactions. Finally, we propose that a reflective HCXAI paradigm—mediated through the perspective of Critical Technical Practice and supplemented with strategies from HCI, such as value-sensitive design and participatory design—not only helps us understand our intellectual blind spots, but it can also open up new design and research spaces.
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Acknowledgements
Sincerest thanks to all past and present teammates of the Human-centered XAI group at the Entertainment Intelligence Lab whose hard work made the case study possible—Brent Harrison, Pradyumna Tambwekar, Larry Chan, Chenhann Gan, and Jiahong Sun. Special thanks to Dr. Judy Gichoya for her informed perspectives on the medical scenarios. We’d also like to thank Ishtiaque Ahmed, Malte Jung, Samir Passi, and Phoebe Sengers for conversations throughout the years that have constructively added to the notion of a ‘Reflective HCXAI’. We are indebted to Rachel Urban and Lara J. Martin for their amazing proofreading assistance. We are grateful to reviewers for their useful comments and critique. This material is based upon work supported by the National Science Foundation under Grant No. 1928586.
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Ehsan, U., Riedl, M.O. (2020). Human-Centered Explainable AI: Towards a Reflective Sociotechnical Approach. In: Stephanidis, C., Kurosu, M., Degen, H., Reinerman-Jones, L. (eds) HCI International 2020 - Late Breaking Papers: Multimodality and Intelligence. HCII 2020. Lecture Notes in Computer Science(), vol 12424. Springer, Cham. https://doi.org/10.1007/978-3-030-60117-1_33
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